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Beyond Static: A Satellite Image Change Detection Using Absolute Convolutional Prior Fusion (AC-PF) Approach

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 11
Year of Publication : 2024
Authors : Aiswarya Jeevan, S Amala Shanthi
10.14445/23488549/IJECE-V11I11P122
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How to Cite?

Aiswarya Jeevan, S Amala Shanthi, "Beyond Static: A Satellite Image Change Detection Using Absolute Convolutional Prior Fusion (AC-PF) Approach," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 11, pp. 272-285, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P122

Abstract:

Urbanization is a dynamic process marked by rapid and intricate land use, infrastructure, and population distribution transformations. Monitoring these changes for sustainable urban development, resource management, and disaster readiness is imperative. Satellite image Change Detection (CD) has emerged as a potent tool for comprehensively and efficiently evaluating urban changes over time. Techniques for CD facilitate identifying, characterising, and quantifying modifications in land cover, land use, and natural phenomena. This capability is pivotal for environmental monitoring, resource management, and disaster response. Detecting changes in urban expansion, deforestation, agricultural practices, and natural disasters contributes to informed decision-making and sustainable development. Whether it is gradual urban expansion or sudden infrastructural developments, the ability to detect changes offers valuable insights into the patterns and drivers of urban growth. Integrating remote sensing technologies and advanced image processing techniques has remarkably enhanced the accuracy and efficiency of CD in urban environments. These methods enable the identification of land cover changes, such as converting green spaces to built-up areas or adjusting transportation networks. This paper introduces an effective CD model that incorporates a deep learning approach. The proposed architecture is directly inspired by the U-Net model, adapted into the AC-PF while considering the available training data. Finally, the Dice similarity score is computed for a specific image compared to the ground truth images and the corresponding input images.

Keywords:

Change detection, Convolutional neural network, Deep learning, Image processing, Remote sensing, Satellite imagery, Urban monitoring.

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